@misc{10481/67456,
year = {2019},
month = {9},
url = {http://hdl.handle.net/10481/67456},
abstract = {This paper focuses on the least-squares linear fusion filter design
for discrete-time stochastic signals from multisensor measurements
perturbed not only by additive noise, but also by different uncertainties
that can be comprehensively modeled by random parameter
matrices. The additive noises from the different sensors are assumed
to be cross-correlated at the same time step and correlated with
the signal at the same and subsequent time steps. A covariancebased
approach is used to derive easily implementable recursive
filtering algorithms under the centralized, distributed and sequential
fusion architectures. Although centralized and sequential estimators
both have the same accuracy, the evaluation of their computational
complexity reveals that the sequential filter can provide a significant
reduction of computational cost over the centralized one. The
accuracy of the proposed fusion filters is explored by a simulation
example, where observation matrices with random parameters are
used to describe different kinds of sensor uncertainties.},
organization = {This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal
de Investigación and Fondo Europeo de Desarrollo Regional FEDER [grant number MTM2017-
84199-P].},
publisher = {Taylor&Francis},
keywords = {Centralized fusion estimation},
keywords = {Distributed fusion estimation},
keywords = {Sequential fusion estimation},
keywords = {Covariance information},
keywords = {Random measurement matrices},
title = {Centralized, distributed and sequential fusion estimation from uncertain outputs with correlation between sensor noises and signal},
doi = {https://doi.org/10.1080/03081079.2019.1659257},
author = {Caballero-Águila, Raquel and Hermoso-Carazo, Aurora and Linares-Pérez, Josefa},
}